Chances are, even before clicking into this guide, you’ve heard the term “data” multiple times already today. Data is one of the most precious resources for any organization, especially for those who work within IT. Why? Its capabilities are endless.
Data is used across industries for many innovative use cases. Data helps machines learn and inspires potential customers to convert. It predicts machine failure, enables AI chatbots, and even improves healthcare. It’s also plentiful. According to recent statistics, global data volume is expected to reach 180 zettabytes by 2025.
But what is data? This question may conjure up images of spreadsheets featuring endless rows and columns of numbers. However, data is much more than that. We must understand data’s various types, categories, and use cases to utilize it to its fullest potential.
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What is Data?
The definition of data is quite simple. According to Merriam-Webster, data is “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation” or “information in digital form that can be transmitted or processed.”
The term “data” can refer to raw data, which is the initial unprocessed and uncleaned version of data, or processed data, which is data that’s been translated into a usable format.
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What are the 7 Key Data Types?
Data is organized into two main types: qualitative and quantitative.
Inside those two types are various categories of data, ranging from nominal data to interval data.
Qualitative data is data that isn’t typically expressed in number form but is most often expressed in words. It’s descriptive in nature and describes qualities that can’t be measured.
Examples of qualitative data may include everything from colors to gender. Qualitative data can be gathered in many ways, such as via surveys, customer interaction, or through observation. It’s then analyzed through a process known as qualitative data analysis.
Inside the qualitative data type are two key categories: nominal and ordinal data.
Nominal data is a form of qualitative data that is perhaps the simplest data type. Nominal data can be grouped into various distinct categories yet isn’t ordered. Examples of nominal data include the state you live in or even your favorite film genre.
Ordinal data is a form of qualitative data that is grouped into categories. However, there is an order to this data based on category. Some common examples of ordinal data include the letter grading system or a customer satisfaction rating.
In contrast to qualitative data, quantitative data can be expressed in numerical form and can be easily measured. Examples of quantitative data include financial data, distance, age, and the number of products sold. If the data can be quantified, it’s quantitative.
Inside the quantitative data type, there are three key categories: discrete, continuous, and interval data.
Discrete data is quantitative data that can be counted using whole numbers (integers). It can’t be expressed as a fraction or decimal. For example, the number of monthly returning customers is an example of discrete data, as you can’t have half or a third of a customer.
Continuous data is quantitative data that can be expressed using decimals and fractions, as accuracy is critical. It’s not necessarily countable, but measurable. While discrete data stays the same over a set period of time, continuous data may change over the same period. Examples of continuous data include characteristics such as height.
Interval data is quantitative data that is measured along a scale using intervals or points that are an equal distance from one another. A common type of interval data is time, where the distance between points (such as 12:00 to 1:00 and 1:00 to 2:00) are equal.
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Where Does Data Come From?
Data is collected from many sources. Some common sources of data include:
Social Media Activity: Public data is one of the most common sources of data. And a massive amount of public data is sourced from what we include on social media accounts, such as Facebook, Twitter, and Instagram. Data can also be gleaned from the websites we frequent such as Google.
Financial Transactions: Companies often use customer purchasing data to inform marketing initiatives and future product development. Data gleaned from these transactions includes payment methods, products purchased, consumer demographics, and more.
Internet of Things (IoT) Sensors: The Internet of Things refers to the network of connected devices, such as smartphones, wearables, and IoT sensors. These sensors are often used in industries such as manufacturing to collect data on machine function and production.
Customer Interactions: Organizations typically gather data from customer interactions, whether via chatbot, email, or phone call. Data may include everything from customer names to what was covered during the interaction. This data can then be used for improving customer service, training, and a wide range of other initiatives. Customer data can also be gathered directly through surveys and questionnaires.
How is Data Used Today? 4 Common Use Cases
Once data is collected, it can then be analyzed and used in a variety of ways. This is true regardless of industry. In fact, data analytics is making waves in all markets, from healthcare to manufacturing.
Machine learning refers to a machine’s ability to “learn” with the support of historical data. By inputting data, algorithms can be trained to find patterns in future data to detect anomalies and make decisions.
Machine learning has endless use cases in all industries. For example, financial institutions can better protect consumers from fraud by using machine learning models that automatically detect fraudulent activity on accounts.
Artificial intelligence is now mainstream and used in a variety of ways. However, AI isn’t possible without enormous amounts of data. For example, ChatGPT, a groundbreaking AI language model, requires massive amounts of language data to work. This data is gleaned from sources such as Internet articles and other texts.
Other examples of AI include virtual assistants, robotics, facial recognition, and even the autocorrect we use daily. All of these examples require data in its many forms to function.
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Business intelligence refers to the process of using data to make informed business decisions. For example, company financial data can be used to inform annual budgets, while customer data can support the development of more effective marketing campaigns.
Customer transactions and CRM software are two key sources of business intelligence data. And through data visualization platforms made specifically for business intelligence, insights are easily visualized in real time via simple charts and reports.
Predictive analytics is the process of using data to predict outcomes and events. For example, in manufacturing, this process can be used to predict when machines will experience unplanned downtime due to malfunction before it occurs. This allows manufacturers to conduct preventative maintenance or repairs before production comes to a halt.
Predictive analytics can also be used in business to predict consumer behavior or in healthcare to predict patient outcomes, which can result in better care.
How to Use Data to Its Fullest Potential
Data can seem complex. However, in today’s data-driven world, data analysis is easier than ever before. Data science platforms can now extract, load, and transform data while surfacing actionable insights without your input. The future is bright for data in all of its forms and the organizations that use it.
Want to dive even deeper into big data and analytics? Check out our guide: What Is Data Analytics?